<i>Patient 1</i> GA-optimized model error. حسب Dmitrii Smirnov (8822324)

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<i>Patient 8</i> GA-optimized model error. حسب Dmitrii Smirnov (8822324)

الموضوعات:

Optimal Drug-Target Docking Visualization. حسب Qijun Wo (12992781)

Algorithm verification. حسب Dmitrii Smirnov (8822324)

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Genetic Algorithm (GA) and CAGE-based personalization block diagrams. حسب Dmitrii Smirnov (8822324)

الموضوعات:

Screening MMRG gene variables associated with UC based on the optimal machine learning model. حسب Li Li (14993)

<i>Patient 2</i> CAGE-based model error. حسب Dmitrii Smirnov (8822324)

الموضوعات:

<i>Patient 9–11</i> RNA-seq based models error. حسب Dmitrii Smirnov (8822324)

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Comparison of presented algorithm precision to Bot et al. حسب Dmitrii Smirnov (8822324)

الموضوعات:

Model convergence to steady state. حسب Dmitrii Smirnov (8822324)

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Key gene identification based on two machine learning algorithms. حسب Lijun Guo (605276)

Distributions of model parameters mirror differentially expressed ion channel genes. حسب Daniele Linaro (227790)

<i>Patient 8</i> GA output model. حسب Dmitrii Smirnov (8822324)

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The flow chart of harmony search-based OED using a double nested loops. حسب Wei Zhang (405)

Table 1_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.pdf حسب Valentina Di Salvatore (6296114)

"…Parallelly, we trained a Random Forest regression model on simulated lipid nanoparticles formulations to predict immune activation values and embedded this model into a genetic algorithm to identify optimal lipid nanoparticles design parameters (size, charge, polyethylene glycol content, and targeting). …"

Presentation 1_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.pdf حسب Valentina Di Salvatore (6296114)

"…Parallelly, we trained a Random Forest regression model on simulated lipid nanoparticles formulations to predict immune activation values and embedded this model into a genetic algorithm to identify optimal lipid nanoparticles design parameters (size, charge, polyethylene glycol content, and targeting). …"

Table 2_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.pdf حسب Valentina Di Salvatore (6296114)

"…Parallelly, we trained a Random Forest regression model on simulated lipid nanoparticles formulations to predict immune activation values and embedded this model into a genetic algorithm to identify optimal lipid nanoparticles design parameters (size, charge, polyethylene glycol content, and targeting). …"

Image 1_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.png حسب Valentina Di Salvatore (6296114)

"…Parallelly, we trained a Random Forest regression model on simulated lipid nanoparticles formulations to predict immune activation values and embedded this model into a genetic algorithm to identify optimal lipid nanoparticles design parameters (size, charge, polyethylene glycol content, and targeting). …"

Image 2_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.png حسب Valentina Di Salvatore (6296114)

"…Parallelly, we trained a Random Forest regression model on simulated lipid nanoparticles formulations to predict immune activation values and embedded this model into a genetic algorithm to identify optimal lipid nanoparticles design parameters (size, charge, polyethylene glycol content, and targeting). …"

Parameter values (relative to baseline O’Hara-Rudy model [12]) of <i>Patient 9</i> models derived via GA or via comparison of mRNA levels to reference patient. حسب Dmitrii Smirnov (8822324)

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